Individuals with an abnormally long QT interval (long QT syndrome, LQTS) have a higher risk of spontaneous fatal arrhythmias [1]. Numerous studies have quantified QT versus RR behavior in various populations to improve methods for risk stratification [2-12]. The accumulative effect of these studies has been to highlight the tremendous variability in the QT-RR relationship and the large associated uncertainty. More recent studies [13, 14] have shown that the QT-RR relationship is patient specific and general trends derived from population studies may lead to faulty interpretations when applied to observations from a single subject.
The measurement of the QT-RR relationship is further complicated by the heart's “memory” of the preceding beat history, resulting in a hysteresis with multiple values of QT associated with single values of RR, depending upon the longer term increasing or decreasing trend of heart rate. Traditional QT-RR curves are based upon the assumption that each QT-RR point on the curve reflects the QT value that would be observed if the heart beat at a constant rate for a sufficiently long period such that all memory of previous beats with different RR intervals was forgotten—a condition that could only exist if the subject's heart was electrically paced. This issue is highlighted in FIG. 1 which shows a more typical clinical example of QT-RR recorded during a cardiac stress test where the patient exhibits five distinct values of QT for RR˜920 msec with a 50 msec range in observed QT values. Previous investigators have attempted to use the observed hysteresis, or difference in QT between the exercise and recovery phases, as a metric for cardiac arrhythmia risk stratification [Starobin, et al. U.S. Pat. No. 7,123,953]. More recent studies [15, 16, 17] have modeled the effective cycle length associated with each QT observation, derived from 24-hour ambulatory ECG (Holter) records, as a sum of weighted RR intervals for the preceding ˜150 beats. The method solves a system of equations where each preceding RR interval weight is an unknown—essentially a system of equations with ˜150 unknowns. This complex methodology is successful in modeling some aspects of patient-specific QT-RR hysteresis.
Many drugs have been found to prolong QT, thus increasing patient risk and driving extensive clinical testing of new therapeutic agents affects on QT duration as part of the Food and Drug Administration (FDA) approval and labeling process. Unexpected prolongation of the QT interval is the major cause of drug withdrawal from the market and for delays in FDA approval. These studies are mainly done using short recordings of patients' resting ECGs taken pre and post administration of the target drug. Because of normal heart rate variability and other factors that may alter the patient's resting rate, the recorded QT values are associated with a range of RR values. To make the assessment of the drug's impact on QT, the recorded values must be corrected to a constant value of RR, know as QTc (corrected QT). The QTc formulas used to correct the raw QT-RR values have been the subject of numerous studies and the community has serious concerns about the validity of the corrections. Further, the scatter in QTc is substantial and the cohort size required to power a statistically meaningful assessment results in very high clinical drug testing costs.
Although the clinical understanding of the observed dispersion and hysteresis of QT-RR is high, there is no simple methodology to develop patient specific QT-RR curves in support of the assessment of patient's risk for LQTS and cardiovascular death or for rapid and cost effective assessment of therapeutic agents.